To address the problem of insufficient detection accuracy of aircraft targets in optical remote sensing images due to complex backgrounds， small targets， and similar appearances among aircraft， an aircraft target detection algorithm was proposed in this paper based on the YOLOv8n model that integrated the global information and the dual-domain attention mechanism in optical remote sensing images. Firstly， the SPPF_Global module was designed to provide a global feature overview through the global maximum pooling layer， which helped the model better distinguish objects from the background in complex environments. Secondly， a dual-domain attention mechanism was proposed to improve the attention to important areas such as wing shape and other distinctive structures through the information guidance of space domain and channel domain， and enhanced the ability to distinguish the nuances of different aircraft models. Finally， the parallel path downsampling method and the Powerful-IoU loss function was introduced， and the adaptive penalty factor was used to accelerate the convergence of the model， which improved the recognition ability of the model for small target aircraft and the regression efficiency of the prediction frame. The experimental results show that compared with the original YOLOv8n， the accuracy rate， recall rate， mAP50 and MAP50-95 of the proposed model on the open data set MAR20 are increased by 3.3%， 2.6%， 3.2% and 2.6% respectively. On the NWPU VHR-10 dataset， the parameters are increased by 5%， 5.1%， 2.5% and 0.3% respectively， while the number of parameters and the calculation amount are decreased by 6.6% and 3.7% respectively， which proves the efficiency and superiority of the proposed model， and effectively improves the application value of the aircraft target detection algorithm in optical remote sensing images.